Tools Menu

The Tools menu provides commands for modifying, analyzing, and synthesizing images.

Destripe

The Destripe command removes striping from an image that contains non-random, detector-dependent noise striping at regular intervals.

Contour Map

The Contour Map command creates, from a single-band input image, a binary output image consisting of white contour lines (lines of equal graylevel values) on a black background. Parameters are the number and width of the contour lines. The width of a contour line is specified as a fraction (a value in the range ) of the spatial width of the adjacent classes.

The graylevel of a pixel in the output image O is computed from the graylevel of the corresponding pixel in the input image I as

where n is the number of contour lines, and w is their width, as specified in the dialog, and the function rnd( ) rounds its real-valued argument to the nearest integer.

If the input image is an elevation model, the result of the Contour Map command is an image showing equal-elevation contours.

The output image is created by dividing the graylevel range of the input image into n+1 subranges (classes) of equal width, where n is the number of contour lines. Each pixel from the input image is assigned to one of those classes. The spatial boundaries between the classes form the contour lines.

Shaded Relief

The Shaded Relief command produces an artificially-shaded view of an original image. Parameters are horizontal illumination angle (azimuth), vertical illumination angle (elevation), and pixel distance (units of elevation). If the input image is an elevation model, the result of the Shaded Relief command is an image simulating a topographic view of the terrain. Shaded Relief is also known as "Hillshading".

Scattergram

The Scattergram command creates an output image representing a two-dimensional histogram ("scattergram") between two bands. The bands may be from either the same or two different images, but must have the same number of lines and pixels/line. They are chosen from two pop-up menus which contain an entry for each band of each image currently open. Additional parameters are the number of bins to be used along both histogram axes, and the pixel and line increment used for sampling in both images. The output image is of size 1-by-n-by-n, where n is the number of histogram bins.

The origin is in the upper left corner. Use the Geometry > Mirror command to flip the image vertically to place the origin in the lower left corner.

The graylevel range of each input image is divided into n subranges of equal size, where n is the number of histogram bins. The graylevel value of a pixel in the output image, , is then computed as the number of pixel locations (m,n) in the input images I and J that have graylevel values in the jth and kth subrange, and , respectively:

As with the Statistics > Histogram command, the subsample increment allows for a tradeoff between accuracy and speed. The typically large dynamic range of image scattergrams usually benefits in visual display from contrast stretches such as Contrast > Logarithmic Stretch, or Contrast > Square Root Stretch (multiple times). The Scattergram command is useful for visualizing the correlation between graylevels in different bands of a multi-band image prior to transforms such as the Principal Components Transform.

RMS Difference

The RMS Difference command computes the root-mean-squared (RMS) difference between two images of the same size. Images to be compared are chosen from two pop-up menus that list all of the images currently open. The two images must have the same number of bands, lines/band, and pixels/line.

The RMS difference between two m-by-n-by-o images I and J is defined as

The RMS difference represents an "average difference" between corresponding pixels in two images. It is equal to zero for two identical images, and increases as the two images become more and more dissimilar. The RMS Difference command is useful for comparing the similarity of two images, or the quality of a processed image relative to a reference image (e.g., in a simulation of sensor degradation and image restoration). It does not, however, correlate particularly well with visual image quality differences.